Glioblastoma (GBM) is the most common malignant glioma in adults. It has an extremely poor prognosis, highlighting an urgent need for new therapeutic strategies to improve patient survival. Lactylation is a novel post-translational modification (PTM) that modulates tumor progression through multiple mechanisms. Yet, research on lactylation in GBM remains limited and fragmented. This study integrated GBM single-cell sequencing data with bulk data from TCGA-GBM, CGGA325, and CGGA693. We first constructed a single-cell lactylation-associated gene expression score (LAGES), and performed subpopulation analysis and functional enrichment on the high and low LAGES groups. Correlation analysis was used to clarify the association between the LAGES and malignant phenotypes, while cell communication analysis was conducted to explore key interaction pathways. We employed 10 machine learning algorithms to build the model, from which we identified the key genes and subsequently constructed a Cox regression analysis model. Meanwhile, the function of the core gene was validated by combining clinical features, functional enrichment, drug sensitivity analysis, and in vitro experiments. Single-cell LAGES effectively stratified GBM cells into high- and low-expression subgroups with distinct functional profiles. High-LAGES tumor cells were enriched in pro-invasive, pro-angiogenic, and immune-suppressive pathways. Low-LAGES cells showed active T cell activation and metabolic homeostasis. Correlation analysis confirmed LAGES was positively associated with GBM malignant phenotypes, including invasion, DNA repair, and epithelial-mesenchymal transition (EMT). Cell communication analysis identified the SPP1-CD44 axis as a key interaction pathway between macrophages and tumor cells. This axis may potentially amplify malignancy in high-LAGES populations, though this inference is exploratory and requires further functional validation. Among the ten machine learning models, the StepCox [backward] + Random Survival Forest (RSF) model exhibited preliminary optimal prognostic performance in the studied cohorts. G6PC3 was identified as the top-ranked core gene closely associated with lactylation. G6PC3 expression increased with glioma grade, which was validated by tissue microarrays. Clinically, high G6PC3 expression correlated with unfavorable features. It also served as an independent poor prognostic factor for IDH-wildtype GBM in the current datasets. Functional enrichment linked G6PC3 to G protein-coupled receptor (GPCR) signaling, calcium ion transport, and exocytosis. Drug sensitivity analysis identified six candidate inhibitors for high-G6PC3 GBM, which are preliminary candidates requiring further preclinical validation. Notably, high G6PC3 expression may tentatively correlate with a potential, unconfirmed higher response to PD-1 inhibitors—a strictly hypothesis-generating finding with no direct clinical predictive value, pending rigorous validation in large, well-characterized ICB-treated cohorts. In vitro experiments confirmed G6PC3 knockdown inhibited GBM cell proliferation, migration, and invasion, while promoting apoptosis. This validated its potential pro-tumorigenic role in vitro, with no in vivo or clinical validation completed. We constructed LAGES to decipher lactylation heterogeneity in GBM and identified G6PC3 as a key gene closely associated with lactylation. G6PC3 may act as a preliminary prognostic biomarker and a strictly hypothesis-generating candidate for exploring potential immunotherapy response in GBM, with no definitive clinical utility implied. Its targeted inhibitors provide a preliminary, exploratory new direction for precision therapy research. Deepening the preliminary understanding of lactylation’s potential role in GBM lays a foundational, exploratory basis for subsequent translational research, rather than implying immediate translational applicability.

The profound intratumoral heterogeneity and the highly dynamic spatiotemporal evolution of its immunosuppressive microenvironment in glioblastoma (GBM) are major factors underlying the limited efficacy of the standard-of-care treatment (SOC). Although immunotherapy has revolutionized the treatment of many solid tumors, it has produced limited clinical benefit in patients with GBM. This limited efficacy is closely associated with the abundance, distribution, and functional state of T cells, which constitute the core effector population mediating antitumor immune responses. However, previous studies have largely focused on the static characteristics of T cells in GBM, whereas the mechanisms governing their spatiotemporal dynamics remain poorly defined, thereby impeding clinical translation. In this review, we delineate the spatiotemporal dynamics of T cells in GBM and re-evaluate the mechanisms underlying immunotherapy failure, thereby identifying potential therapeutic opportunities. Furthermore, we aim to provide new insights into patient stratification, the development of precise targets that modulate T-cell spatiotemporal dynamics, and personalized combination strategies for patients with GBM.

Glioblastoma (GBM) is the most malignant primary central nervous system tumor in adults, with strong invasiveness, high recurrence, and poor prognosis. Natural killer (NK) cells, innate immune cells that eliminate glioma stem cells without MHC matching, show promise for GBM immunotherapy, but their efficacy is limited by GBM's immunosuppressive tumor microenvironment (TME), especially via protein post-translational modifications (PTMs). This review summarizes seven key PTMs' (phosphorylation, acetylation, glycosylation, methylation, ubiquitination, SUMOylation, lactylation) dual regulation on NK cell therapy: physiological PTMs enhance NK cytotoxicity, targeting, and persistence; aberrant PTMs block NK activation, induce exhaustion, and promote GBM immune escape. It also analyzes bottlenecks (insufficient NK activity/persistence, GBM's PTM-mediated escape) and breakthroughs (PTM-targeted small molecules like TAK-981, CRISPR-edited NK cells, combination therapies). Future directions include BBB precision delivery, PTM-guided personalized therapy, and PTM crosstalk research, aiming to advance NK therapy's clinical translation for GBM.

Radiotherapy has significantly improved survival outcomes in glioblastoma (GBM) patients, particularly when combined with tumor resection and temozolomide chemotherapy. However, radioresistance remains a major obstacle, limiting further advances in prognosis and potential cure. DEPDC1, a gene implicated in various malignant phenotypes, has not yet been investigated for its role in GBM radioresistance, despite suggestive evidence from previous studies. In this study, analysis of the cancer database and GBM tissue microarrays revealed that DEPDC1 expression is significantly elevated in GBM tissues compared to normal brain tissues. Using GBM cell lines with DEPDC1 knockdown, we observed markedly reduced proliferation and motility. Notably, DEPDC1 downregulation significantly enhanced radiosensitivity, as demonstrated by increased radiation-induced apoptosis, G2/M phase arrest, DNA damage, and impaired DNA repair post-radiation, resulting in lower survival rates in clonogenic assays. Mechanistically, these effects may be mediated through DEPDC1-driven upregulation of NF-κB, supported by tumor sample analysis from our xenograft mouse model. Collectively, our findings suggest that DEPDC1 is not only involved in GBM malignancy but also represents a promising therapeutic target for overcoming radioresistance and developing effective radiosensitizers. ### Competing Interest Statement The authors have declared no competing interest. National Natural Science Foundation of China, https://ror.org/01h0zpd94, 81772680

Glioblastoma is highly aggressive and resistant to treatment, making it crucial to understand the regulatory mechanisms underlying its invasion. LIN7A, a polar protein, has been implicated in tumor cell migration and invasion, but its role in glioblastoma remains unclear. This study aimed to manipulate LIN7A gene expression in U87 cells, analyze its impact on invasion, and explore the potential mechanisms through which LIN7A regulates glioblastoma cell invasion. Lentiviral vectors were used to silence the LIN7A gene in U87 cells, selecting the most effective vector. LIN7A gene transcription, protein expression and localization were analyzed using RT-qPCR, Western blotting, and immunofluorescence. U87 cell invasion was assessed via real-time cell analysis and spheroid invasion assay, while MMP-2 and MMP-9 protease activities were measured using zymography. β-catenin protein levels and localization were evaluated through Western blotting and immunofluorescence. Expression of target genes in the β-catenin pathway was also measured. An orthotopic xenograft glioblastoma model in nude mice was established by intracranial implantation of U87 cells, with tumor growth monitored using immunofluorescence analysis of brain slices. The clinical significance of LIN7A expression was confirmed by comparing its levels in core and peripheral invading areas of glioblastoma and analyzing RNASeq data and clinical information from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) GBM cohort. Transfection of U87 cells with a lentiviral vector led to decreased LIN7A levels and altered distribution patterns. Silencing the LIN7A gene increased U87 cell proliferation and invasiveness, reduced clonal formation ability, and enhanced MMP-2 and MMP-9 protease activity. It also resulted in a slight increase in cytoplasmic β-catenin content, although not statistically significant, but a significant increase in nuclear β-catenin accumulation and transcriptional activity of target genes in the pathway. Animal studies showed that LIN7A gene silencing caused U87 cells to transition from clumpy to invasive growth mode. LIN7A expression was significantly lower in the peripheral invading area compared to the core area in clinical samples of glioblastoma. Data mining of the CPTAC-GBM cohort revealed a strong association between LIN7A gene expression and survival time. Silencing LIN7A may promote U87 tumor cell invasion by disrupting intercellular junctions, altering cell polarity, and activating the β-catenin pathway. Further research is warranted to elucidate the role of LIN7A in glioblastoma cell invasion.

Background: Increased fatty acid metabolism (FAM) is an important marker of tumor metabolism. However, the characterization and function of FAM-related genes in glioblastoma (GBM) have not been fully explored. Method: In the TCGA-GBM cohort, FAM-related genes were divided into three clusters (C1, C2, and C3), and the DEGs between the clusters and those in the normal group and GBM cohort were considered key genes. On the basis of 10 kinds of machine learning methods, we used 101 combinations of algorithms to construct prognostic models and obtain the best model. In addition, we also validated the model in the GSE43378, GSE83300, CGGA, and REMBRANDT datasets. We also conducted a multifaceted analysis of F13A1, which plays an important role in the best model. Results: C2, with the worst prognosis, may be associated with an immunosuppressive phenotype, which may be related to positive regulation of cell adhesion and lymphocyte-mediated immunity. Using multiple machine learning methods, we identified RSF as the best prognostic model. In the RSF model, F13A1 accounts for the most important contribution. F13A1 can support GBM malignant tumor cells by promoting fatty acid metabolism in GBM macrophages, leading to a poor prognosis for patients. This metabolic reprogramming not only enhances the survival and proliferation of macrophages, but also may promote the growth, invasion, and metastasis of GBM cells by secreting growth factors and cytokines. F13A1 is significantly correlated with immune-related molecules, including IL2RA, which may activate immunity, and IL10, which suggests immune suppression. F13A1 also interferes with immune cell recognition and killing of GBM cells by affecting MHC molecules. Conclusions: The prognostic model developed here helps us to further enhance our understanding of FAM in GBM and provides a compelling avenue for the clinical prediction of patient prognosis and treatment. We also identified F13A1 as a possibly novel tumor marker for GBM which can support GBM malignant tumor cells by promoting fatty acid metabolism in GBM macrophages.

Glioblastoma, the most common and aggressive primary malignant brain tumor, is characterized by a high rate of recurrence, disability, and lethality. Therefore, there is a pressing need to develop more effective prognostic biomarkers and treatment approaches for glioblastoma. Lactylation, an emerging form of protein post-translational modification, has been closely associated with lactate, a metabolite of glycolysis. Since the initial identification of lactylation sites in core histones in 2019, accumulating evidence has shown the critical role that lactylation plays in glioblastoma development, assessment of poor clinical prognosis, and immunosuppression, which provides a fresh angle for investigating the connection between metabolic reprogramming and epigenetic plasticity in glioblastoma cells. The objective of this paper is to present an overview of the metabolic and epigenetic roles of lactylation in the expanding field of glioblastoma research and explore the practical value of developing novel treatment plans combining targeted therapy and immunotherapy.

Treatment of gliomas, the most prevalent primary malignant neoplasm of the central nervous system, is challenging. Arachidonate 5-lipoxygenase activating protein (ALOX5AP) is crucial for converting arachidonic acid into leukotrienes and is associated with poor prognosis in multiple cancers. Nevertheless, its relationship with the prognosis and the immune microenvironment of gliomas remains incompletely understood. The differential expression of ALOX5AP was evaluated based on public Databases. Kaplan–Meier, multivariate Cox proportional hazards regression analysis, time-dependent receiver operating characteristic, and nomogram were used to estimate the prognostic value of ALOX5AP. The relationship between ALOX5AP and immune infiltration was calculated using ESTIMATE and CIBERSORT algorithms. Relationships between ALOX5AP and human leukocyte antigen molecules, immune checkpoints, tumor mutation burden, TIDE score, and immunophenoscore were calculated to evaluate glioma immunotherapy response. Single gene GSEA and co-expression network-based GO and KEGG enrichment analysis were performed to explore the potential function of ALOX5AP. ALOX5AP expression was verified using multiplex immunofluorescence staining and its prognostic effects were confirmed using a glioma tissue microarray. ALOX5AP was highly expressed in gliomas, and the expression level was related to World Health Organization (WHO) grade, age, sex, IDH mutation status, 1p19q co-deletion status, MGMTp methylation status, and poor prognosis. Single-cell RNA sequencing showed that ALOX5AP was expressed in macrophages, monocytes, and T cells but not in tumor cells. ALOX5AP expression positively correlated with M2 macrophage infiltration and poor immunotherapy response. Immunofluorescence staining demonstrated that ALOX5AP was upregulated in WHO higher-grade gliomas, localizing to M2 macrophages. Glioma tissue microarray confirmed the adverse effect of ALOX5AP in the prognosis of glioma. ALOX5AP is highly expressed in M2 macrophages and may act as a potential biomarker for predicting prognosis and immunotherapy response in patients with glioma.

[目的]基于单细胞测序筛选胶质母细胞瘤特征基因并构建预后模型.[方法]分析GEO数据库单细胞RNA测序数据集GSE84465,筛选出GBM细胞分化相关的差异基因.下载TCGA数据库GBM的基因表达谱和临床数据,采用Lasso回归、Cox回归分析筛选出特征基因构建预后模型,根据独立预后因素构建列线图,GSE83300作为外部验证集.基于风险评分中位数将患者分组,比较两组生存差异.[结果]通过scRNA-seq得到492个分化差异基因,经过回归分析得到基于6个基因(PLAUR、RARRES2、G0S2、MDK、SERPINE2、CD81)的预后模型.其1、3、5年ROC曲线下面积均大于0.7;KM分析显示高低风险组预后存在差异(P＜0.001),GSE83300验证结果与TCGA 一致.多因素Cox回归分析表明年龄和风险评分可以作为独立影响因素(P＜0.01);C-Index(0.679)、校准图显示列线图预测模型有良好的拟合度.GSEA分析示高低风险组差异基因集参与细胞因子受体相互作用、抗原处理与提呈等通路.[结论]由PLAUR、RARRES2、G0S2、MDK、SERPINE2、CD81 构建的模型能够预测 GBM 患者预后.

目的:探讨转化生长因子β诱导蛋白(TGFBI)在胶质瘤中的表达及其生物学功能.方法:通过从癌症基因组图谱(TCGA)、中国脑胶质瘤图谱(CGGA)和脑肿瘤分子数据库(Rembrandt)下载胶质瘤的转录组数据和相应的临床资料,分析在不同级别胶质瘤中TGFBI mRNA的表达水平,及其表达变化与胶质瘤患者预后的关系.免疫组织化学染色法检测35例胶质母细胞瘤组织和5例正常脑组织中TGFBI蛋白的表达水平.通过用慢病毒转染U87和U373细胞株构建TGFBI表达下调的胶质母细胞瘤细胞系,检测TGFBI对胶质瘤细胞增殖、侵袭、凋亡和细胞周期的影响.通过基因集富集分析(GSEA)预测TGFBI在胶质瘤发生发展中可能参与并调控的相关通路.结果:TCGA、CGGA和Rembrandt数据库的结果分析显示随着胶质瘤级别的增加,TGFBI mRNA的表达水平升高(P<0.05).此外,TGFBI mRNA的高表达与胶质瘤患者不良预后显著相关(P<0.01).免疫组化检测结果显示,与正常脑组织相比,胶质母细胞瘤组织中TGFBI蛋白表达水平显著增加(P<0.05).细胞学实验结果显示,TGFBI表达下调可显著抑制胶质母细胞瘤细胞增殖和侵袭能力,同时能够促进细胞凋亡,促使细胞从G1期转向S期.GSEA分析结果显示高表达TGFBI可能通过TOLL样受体信号通路、NOD样受体信号通路和JAK-STAT信号通路参与调控胶质瘤恶性生物学行为.结论:TGFBI在胶质母细胞瘤组织中表达升高,可促进肿瘤细胞的增殖、侵袭,抑制细胞凋亡并诱导细胞周期重排,推测其可能在胶质瘤发生发展中扮演着癌基因的角色.

Objective To explore the epidemiological characteristics of patients with lymphoepithelial carcinoma (LEC) of the head and neck and the prognostic factors. Methods We conducted a retrospective cohort study of cases of head and neck LEC retrieved from the Surveillance, Epidemiology and End Results database. Kaplan–Meier survival analysis and the log-rank test were employed to assess overall survival (OS) and cancer-specific survival (CSS). Univariate and multivariate analyses were used to construct Cox regression models. We established nomograms to predict OS and CSS among patients with nasopharyngeal LEC, who were divided into high- and low-risk groups based on the OS nomograms to compare the effects of treatment using the restricted mean survival time (RMST). Results The 5-year OS and CSS rates of the cohort were 70.8% and 74.8%, respectively. Advanced age, unmarried status, black race, distant metastasis, and the absence of surgical treatment were significantly associated with decreased survival rates. RMST did not differ between the combined treatment (radiotherapy and chemotherapy) and radiotherapy monotherapy groups, but chemotherapy alone displayed poor efficacy. Conclusions Head and neck LEC is associated with a favorable prognosis. Radiotherapy plays a significant role in managing patients with nasopharyngeal LEC, which is influenced by multiple prognostic factors.

高级别胶质瘤(high-grade glioma,HGG;WHO分级Ⅲ、Ⅳ级),恶性程度高,侵袭性强[1],即使采用手术、放疗和化疗等综合治疗,但效果仍然不佳[2],中位生存期不足15个月[3].研究发现,乳腺癌[4]、子宫内膜癌[5]及结直肠癌[6]等恶性肿瘤广泛存在细胞间网络连接,在肿瘤的侵袭和复发中起着重要作用.胶质瘤也存在类似的网络连接[7].本文就细胞网络连接在HGG进展中的作用研究进展进行综述.

目的 探讨生长相关蛋白43(GAP43)的表达对胶质母细胞瘤(GBM)替莫唑胺(TMZ)化疗敏感性的影响及机制.方法 根据表达GAP43的水平将S24-GBM干细胞系(S24-GBMSCs)分成3个组:sh-gap43组(敲除),对照组和gap43组(过表达).测定TMZ对离体S24-GBMSCs生物学行为的影响;将细胞移植后进行TMZ化疗,对比肿瘤体积的差异和荷瘤裸鼠的生存时间,组织学分析细胞的增殖和凋亡率,检测细胞内丝裂原活化蛋白激酶信号通路的激活.组间采用t检验或单因素方差分析,生存时间采用Kaplan-Meier分析.结果 sh-gap43组细胞团经TMZ处理后36h(0.77±0.18)和 72h(0.55±0.12)后相对直径均明显小于对照组(1.45±0.11、1.64±0.19,t=12.753、18.694,P＜0.05、0.01),而 gap43 组相对直径均显著高于对照组(36 h:1.86±0.21,t=7.346,P＜0.05;72h:2.45±0.16,t=6.483,P＜0.05);体内成瘤实验中,TMZ 化疗后,sh-gap43 组(0.05±0.04)肿瘤相对体积均显著小于非化疗组(0.18±0.09,t=5.270,P＜0.01),中位生存时间(112 d)也明显长于非化疗组(103 d,x2=7.833,P＜0.05);gap43组化疗后肿瘤相对体积和中位生存时间与非化疗组比较差异无统计学意义;组织学染色显示,TMZ化疗后,sh-gap43组[(10.3±4.9)％]细胞核增殖抗原(Ki-67)的表达率低于相应的非化疗组[(18.1±4.0)％,t=6.426,均P＜0.05];细胞凋亡蛋白酶(Caspase)-3的表达率[(27.4±0.4)％]显著高于相应的非化疗组[(4.0±0.1)％,t=6.389,P＜0.01];组织蛋白质印迹法(Western blot)可见,gap43组化疗后c-Jun氨基末端激酶(JNK)磷酸化水平(0.88±0.07)明显高于非化疗组(0.52±0.10,t=7.413,P＜0.01);而sh-gap43组化疗后JNK磷酸化水平(0.28±0.08)明显低于非化疗组(0.51±0.07,t=5.274,P＜0.05).结论 GAP43降低S24-GBM对TMZ化疗的敏感性,其机制与JNK信号通路激活有关.

目的 筛选低级别胶质瘤(LGG)预后相关的免疫lncRNA,构建免疫相关lncRNA预后风险模型.方法 从公共数据库TCGA下载LGG转录组数据及相应的临床信息,用R语言以共表达法获取免疫相关lncRNA,单因素和多因素Cox回归分析筛选得到有预后价值的免疫相关lncRNA,并以其构建风险模型.根据风险值将患者划分为高风险组和低风险组,采用Kaplan-Meier法进行生存分析并绘制生存曲线图,使用ROC曲线对风险模型的准确性进行评估.同时采用单因素和多因素Cox回归法分析风险评分和其它临床因素与LGG患者生存预后的关系.通过Cibersort软件计算22种免疫浸润细胞在高、低风险分组中的相对比例.最后对风险模型中4个lncRN A与主要的免疫检查点分子进行相关性分析.结果 通过免疫基因-lncRNA共表达网络筛选出79个免疫相关lncRNA,利用单因素Cox回归筛选出8个有预后价值的免疫相关lncRNA,基于多因素Cox回归分析最终确定4个关键lncRNA(RFPL1S、AC145098.1、AC090559.1、TGFB2-AS1),并构建风险模型.根据中位风险值将患者分为高风险组和低风险组,生存分析显示两组生存时间存在显著差异(P<0.01),预后风险模型曲线AUC值为0.788.多因素Cox回归分析显示患者年龄、肿瘤级别和风险分数均是预后不良的独立危险因素.Cibersort法分析结果显示高风险组LGG患者肿瘤中有较多的单核细胞和M2型巨噬细胞浸润.相关性分析显示模型中的4个lncRNA与PD1、PD-L1、CD47及CTLA4之间存在较强的相关性(均P<0.05).结论 通过生物信息分析技术成功构建基于lncRNA表达水平的LGG患者预后模型,所确定的4个关键lncRNA有望成为判断LGG患者预后的指标和潜在治疗靶点.

胶质母细胞瘤是中枢神经系统恶性程度最高的胶质瘤,其标准治疗手段是最大程度的肿瘤切除、放疗和替莫唑胺辅助化疗,中位生存期仅为14个月.免疫疗法开启多种癌症治疗的新篇章,在胶质母细胞瘤中却未能带来生存获益.胶质母细胞瘤具有高度异质性和复杂的免疫抑制性微环境,肿瘤细胞与非肿瘤细胞相互作用,促进肿瘤的生长、侵袭、耐药.因此,剖析胶质母细胞瘤免疫微环境各组成成分,了解免疫细胞与肿瘤细胞之间的信号通路,有利于理解免疫治疗失败的原因,也为后续研究提供依据.

Annexin-1 (ANXA1) is widely reported to be deregulated in various cancers and is involved in tumorigenesis. However, its effects on glioblastoma (GBM) remain unclear. Using immunohistochemistry with tissue microarrays, we showed that ANXA1 was overexpressed in GBM, positively correlated with higher World Health Organization (WHO) grades of glioma, and negatively associated with poor survival. To further explore its role and the underlying molecular mechanism in GBM, we constructed ANXA1shRNA U87 and U251 cell lines for further experiments. ANXA1 downregulation suppressed GBM cell proliferation, migration, and invasion and enhanced their radiosensitivity. Furthermore, we determined that ANXA1 was involved in dendritic cell (DC) maturation in patients with GBM and that DC infiltration was inversely proportional to GBM prognosis. Considering that previous reports have shown that Interleukin-8 (IL-8) is associated with DC migration and maturation and is correlated with NF-κB transcriptional regulation, we examined IL-8 and p65 subunit expressions and p65 phosphorylation levels in GBM cells under an ANXA1 knockdown. These results suggest that ANXA1 significantly promotes IL-8 production and p65 phosphorylation levels. We inferred that ANXA1 is a potential biomarker and a candidate therapeutic target for GBM treatment and may mediate tumour immune escape through NF-kB (p65) activation and IL-8 upregulation.

China has a heavy burden of hepatocellular carcinoma, which is a serious threat to people′s life and health. However, the available drugs for advanced hepatocellular carcinoma in the past are limited and the efficacy is not satisfactory. In recent years, immunotherapy has a significant effects in some tumors. The authors introduce the efficacy of restart immunotherapy on an advanced hepatocellular carcinoma patient undergoing interruption of treatment due to corona virus disease 2019, in order to provide references for the diagnosis and treatment of this kind of patients.

Background : Glioblastoma(GBM) is a common primary malignant brain tumor with poor prognosis, and currently effective therapeutic strategies are still limited. RNA binding proteins(RBPs) dysregulation has been reported in various cancers and is closely related to tumor initiation and progression. However, little is known about the role of RBPs in GBM. Methods : We downloaded RNA-seq transcriptome from TCGA database and differently expressed RBPs were screened between tumor and normal tissues. Then we performed functional enrichment analysis of these RBPs and based on univariate and multivariate cox regression analysis, hub RBPs were identified. Furthermore, we constructed a risk model based on hub RBPs and divided patients into high- and low-risk groups based on the median risk score. To validate the model, CGGA database were conducted as a training set and then both survival analysis and ROC curve were conducted. We also developed a nomogram based on five RBPs, which made more convenient to observe each patient’s prognosis and validated the connection between patients survival and each hub RBP . Finally, we used GEPIA website to further explore the value of these hub RBPs. Results : A total 309 differently expressed RBPs were identified, including 145 downregulated and 164 upregulated RBPs. and the result indicated that they were mainly enriched in mRNA processing, RNA splicing, RNA catabolic process, RNA transport, spliceosome, ribosome and mRNA surveillance pathway. Five hub RBPs were identified and we observed that patients with high risk score were related to poor overall survival and the AUC of ROC curve was 0.752 in TCGA. The result was subsequently proved by CGGA, showing the good prediction function of the model. Then GEPIA website suggested that MRPL41, MRPL36 and FBXO17 were closely associate with OS in GBM. Conclusion : Our result may provide novel insights into pathogenesis of GBM and development of new therapeutic targets. However, further experiments in vitro and in vivo will be warranted.

Dear Editor, The pandemic of coronavirus disease 2019 (COVID-19) has stressed and overloaded the existing medical capacity worldwide. From a more pragmatic perspective, the early detection of patients who may experience rapid clinical deterioration will enable prompt interventions and avert disease progression.1 T cell exhaustion, immunothrombotic dysregulation, as well as complement-associated microvascular injury are considered as the hallmarks of disease severity in COVID-19.2-5 It is generally accepted that the identification of useful surrogates, for example, IL-6, TNFα, MIP1α, LDH, ferritin, D-dimer, CK, etc., to represent as immune response to COVID-19 infection is crucial.3, 4, 6 Nevertheless, no individual parameter was so far predictive of immune-thrombotic dysregulation fueled by a maladaptive host inflammatory response in severe infection with SARS-CoV-2.7-9 We, therefore, consider to develop potential solutions for forecasting thrombotic complications prior to clinicopathological exacerbation. By incorporating whole blood transcriptome profiling and multi-omics analysis, our study characterized immunological and hematological perturbations with respect to different categories of severity (i.e., healthy donors vs. mild or moderate vs. severe vs. critical illness). Functional diversity was found among those groups by unsupervised hierarchical clustering of differential expression profiles (Figure 1A, left). Circus plots revealed that the differentially expressed genes (DEGs) were enriched into the key processes, that is, neutrophil activation, platelet activation, blood coagulation, complement receptor-mediated signaling pathway, leukocyte activation, and cytokines production. In contrast, the downregulated DEGs were functionally linked with lymphocyte activation/proliferation/differentiation/migration, gamma delta (γδ) and alpha beta (αβ) T cells activation, and so on (Figure 1A, right, and B). More specifically, the upregulation of gene-signatures in platelet, neutrophil, and coagulation activation, as well as downregulation of lymphocyte activation in severe and critically ill COVID-19 were demonstrated (Figure 1B). Multi-omics data incorporating plasma cytokines and chemokines, circulating complements, flow cytometry-derived immune cells counts, clinical laboratory outcomes, as well as featured gene-signatures were implicated in pairwise Pearson correlations (Figure 1C, left). Furthermore, the upregulations of both neutrophil and platelet activation signatures were strongly correlated with downregulation of lymphocyte activation (R = –0.88, p < 0.001) (Figure 1C, middle). Gene-subsets for neutrophil, platelet, and coagulation activations were found to correlate with blood complements C3b, C4a, C6b, and C7b, in contrast to lymphocytes as inverse correlations (Figure 1C, right, and 1E, left). DEGs with specific interests to the recruitment and activation of neutrophils and platelets were also studied. A spectrum of genes were identified in initiation and amplification of the proinflammatory response, immune complex-mediated activation of neutrophils, acting as cell surface receptors or their intracellular signal transductions for platelets and neutrophils, for example, S100As, SERPINA1, TLRs, STAT3, SELP (P-selectin), SELPLG (PSGL-1), SYK, F2RL1 (PAR2), ITGAM (αM), ITGB2 (β2), ITGA2B (αIIb), ITGB3 (GPIIIa), and so on. The key molecules associating with NET formation (NETosis), including PAD4, FCGR2A (FcγRIIa), PLCG2 (PLCγ2), CFP, F8, and F12, were considerably upregulated, facilitating platelets–neutrophils conjugates and highly procoagulant microcirculation disturbances via intrinsic pathways.10 Those transcriptional signatures were also partially evidenced in the proteomics level by Tian et al.11 Intriguingly, neutrophil effector molecules, such as ELANE (neutrophil elastase), MPO (myeloperoxidase), CTSG (Cathepsin G), as well as vascular inflammation mediator PTX3 and neutrophil-derived lactoferrin, were significantly upregulated in severe compared to critical illness (Figure 1D). NETs were described as important mediators of coagulation.12 Neutrophil activations correlated well with NETosis (R = 0.98, p < 0.0001), as well as blood D-dimer concentrations (R = 0.78, p < 0.001), highlighting a prominent role of activated neutrophils or NETosis in the pathogenesis of COVID-19 coagulopathy (Figure 1E, right). The unveiled transcriptional findings were validated in a multicenter cohort of 1219 eligible individuals (Figure S1). A summary of patient characteristics is provided (Table S1). Peripheral lymphocyte, neutrophil, platelet counts, as well as hemoglobin and ages among different severity groups were shown (Figure 2A–E). Besides, the demographically predictive of protection against advancement of severity in COVID-19 is female sex, particularly for critically ill and lethal events (Figure S2). Consistent with transcriptional findings, clinical laboratory outcomes evidenced that lymphopenia, neutrophilia, as well as thrombocytopenia owning to the overconsumption of platelets were notably characterized in the late stages of COVID-19. And those features were of mutual linkages and exhibited correlation to varying degrees (Figure 2F). A three-dimensional simulation further implicated the dynamic interplay of lymphocyte, neutrophil, platelet, and hemoglobin (Figure 2G), providing a solid basis for mathematical modeling. Nonetheless, an individual blood parameter had relatively poor predictive performance for stratifying patients with different severity (Figure 2H–J and Table S2). To improve the discrimination accuracy, machine learning-based severity classification was performed. LASSO regression classifier was applied to train the model utilizing the featured blood-parameters (Figure 3A). The calibration curve demonstrated a good consistence between the predicted and observed values and favorable predictive performance confirmed by receiver operating characteristic (ROC) analysis (Figure 3B–D). The discriminative ability was also assessed for testing and validation cohorts (Figure S3A–H). In parallel, the generalized linear model (GLM) and linear discriminant analysis (LDA) were utilized for the construction and optimization of disease discrimination. Strong discriminative capacities were achieved for both GLM (Figure 3E) and LDA (Figure 3F)-based algorithms. Eventually, the overall cohort of 1219 patients was stratified into different degrees of severity with a robust hierarchical classification capacity (Figure 3G). Machine learning-based prognosis prediction was also studied (Figure 4A). The calibration curve and the diagonal coincided in general, indicating relatively high prediction accuracy for 15-, 30-, and 45-days in-hospital mortality risks (Figure 4B). A superior prediction capacity was demonstrated by decision curve analysis (DCA) and the net reduction in interventions was maximized (Figure 4C, D). The derived survival risk score was associated with immunethrombotic dysregulation. Patients in the training cohort could be, therefore, divided into high- and low-risk groups with significantly stratified fatal risks (Figure 4E). Area under curves (AUCs) of 0.73 (95% CI, 0.65–0.81), 0.80 (95% CI, 0.73–0.87), and 0.81 (95% CI, 0.72–0.90) for 15-, 30-, and 45-days were determined, and distinct survival outcomes were observed (p < 0.0001) (Figure 4F, G). Consistently, highly predictive performance was also evaluated in both internal testing (Figure 4H–J) and external validation cohort (Figure 4K–M). In conclusion, genome-wide whole blood profiling was performed to deciphering the peripheral immune and hematologic pertubations to COVID-19, revealed an interesting feature of uncontrolled neutrophil-complement-coagulation interplay associated with immunethrombosis in severe and critically ill patients. Via machine learning techniques as well as the inclusion of large-scale multicenter cohorts of 1219 patients, an optimized precision of prediction algorithm by integrating platelet, neutrophil, and lymphocyte counts and hemoglobin was established. Taken together, we developed and validated mechanistic-driven rather than purely data-driven algorithms to assess the specific risks of immunothrombotic dysregulation in COVID-19. In principle, it might be used as a potential surrogate of decision-making for the ICU patients with coagulation abnormalities, enabling more timely interventions, such as low molecular weight heparin-treatment, and/or anticytokine therapies. Of note, those patients in ICUs are largely incapable of communicating and with very limited access to standard imaging utilizing computed tomography (CT). This algorithm will assist in guiding clinical decision-making in more individualized managements and provide insights for longitudinal surveillance of severe and critically ill individuals. This work was supported by the National Natural Science Foundation of China (NSFC) (No. 81703166), Science and Technology Program of Guangzhou (Nos. 202002030445 and 202002030086), Natural Science Foundation of Guangdong Province (No. 2019A1515011943), China Postdoctoral Science Foundation (Nos. 2020T130052ZX and 2019M662974), and Medical Scientific Research Foundation of Guangdong Province (Nos. A2020505, A2020499, B2021203, and B2021139). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. The authors declare no potential conflicts of interest. This study was approved by the Ethics Committee of Nanfang Hospital, Southern Medical University (approval number: NFEC-2020-033) and the Ethics Committees from the collaborated centers. CZ, ZZ, PZ, and LW conceived and designed the study. XZ, LMC, TA, HG, HD, QY, YJL, YXL, XC, BN, SW, XLZ, JL, MXZ, and HY assisted in acquisition, analysis, and interpretation of the data. ZZ, LW, CZ, DG, and CJ developed and validated the algorithms. ZZ, LW, DG, XZ, and LMC did the statistical and transcriptome analysis under the supervision of CZ, LHC, LBC, MLL, MJZ, and PZ. CZ, ZZ, and LW wrote the manuscript. BJ, AA, PZ, and LZ revised critically the study for important intellectual content. All authors have read and approved the final study. The transcriptome sequencing data was deposited at the Gene Expression Omnibus under the accession number GSE167930. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.